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GB201805296D0 - Shortlist Selection Model For Active Learning - Google Patents

Shortlist Selection Model For Active Learning

Info

Publication number
GB201805296D0
GB201805296D0 GBGB1805296.9A GB201805296A GB201805296D0 GB 201805296 D0 GB201805296 D0 GB 201805296D0 GB 201805296 A GB201805296 A GB 201805296A GB 201805296 D0 GB201805296 D0 GB 201805296D0
Authority
GB
United Kingdom
Prior art keywords
shortlist
selection model
active learning
learning
active
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
GBGB1805296.9A
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
BenevolentAI Technology Ltd
Original Assignee
BenevolentAI Technology Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by BenevolentAI Technology Ltd filed Critical BenevolentAI Technology Ltd
Priority to GBGB1805296.9A priority Critical patent/GB201805296D0/en
Publication of GB201805296D0 publication Critical patent/GB201805296D0/en
Priority to US17/041,622 priority patent/US12094578B2/en
Priority to CN201980033302.XA priority patent/CN112136179B/en
Priority to EP19716525.1A priority patent/EP3776563A2/en
Priority to PCT/GB2019/050924 priority patent/WO2019186195A2/en
Ceased legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/70—Machine learning, data mining or chemometrics
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G06F18/2155—Generating training patterns; Bootstrap methods, e.g. bagging or boosting characterised by the incorporation of unlabelled data, e.g. multiple instance learning [MIL], semi-supervised techniques using expectation-maximisation [EM] or naïve labelling
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/20—Ensemble learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/042—Knowledge-based neural networks; Logical representations of neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/091—Active learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
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    • G06N3/08—Learning methods
    • G06N3/092—Reinforcement learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/01—Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/02—Knowledge representation; Symbolic representation
    • G06N5/022—Knowledge engineering; Knowledge acquisition
    • G06N5/025—Extracting rules from data
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/10—Analysis or design of chemical reactions, syntheses or processes
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/043—Architecture, e.g. interconnection topology based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/047—Probabilistic or stochastic networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/088—Non-supervised learning, e.g. competitive learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/046—Forward inferencing; Production systems
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • General Health & Medical Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Molecular Biology (AREA)
  • Chemical & Material Sciences (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Medical Informatics (AREA)
  • Databases & Information Systems (AREA)
  • Analytical Chemistry (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Evolutionary Biology (AREA)
  • Investigating Or Analysing Biological Materials (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Machine Translation (AREA)
GBGB1805296.9A 2018-03-29 2018-03-29 Shortlist Selection Model For Active Learning Ceased GB201805296D0 (en)

Priority Applications (5)

Application Number Priority Date Filing Date Title
GBGB1805296.9A GB201805296D0 (en) 2018-03-29 2018-03-29 Shortlist Selection Model For Active Learning
US17/041,622 US12094578B2 (en) 2018-03-29 2019-03-29 Shortlist selection model for active learning
CN201980033302.XA CN112136179B (en) 2018-03-29 2019-03-29 Candidate List Selection Model for Active Learning
EP19716525.1A EP3776563A2 (en) 2018-03-29 2019-03-29 Shortlist selection model for active learning
PCT/GB2019/050924 WO2019186195A2 (en) 2018-03-29 2019-03-29 Shortlist selection model for active learning

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
GBGB1805296.9A GB201805296D0 (en) 2018-03-29 2018-03-29 Shortlist Selection Model For Active Learning

Publications (1)

Publication Number Publication Date
GB201805296D0 true GB201805296D0 (en) 2018-05-16

Family

ID=62142218

Family Applications (1)

Application Number Title Priority Date Filing Date
GBGB1805296.9A Ceased GB201805296D0 (en) 2018-03-29 2018-03-29 Shortlist Selection Model For Active Learning

Country Status (5)

Country Link
US (1) US12094578B2 (en)
EP (1) EP3776563A2 (en)
CN (1) CN112136179B (en)
GB (1) GB201805296D0 (en)
WO (1) WO2019186195A2 (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2020198003A (en) * 2019-06-04 2020-12-10 ジャパンモード株式会社 Product estimation program and system
DE102019208121A1 (en) * 2019-06-04 2020-12-10 Continental Automotive Gmbh Active data generation taking into account uncertainties
US11829871B2 (en) * 2019-08-20 2023-11-28 Lg Electronics Inc. Validating performance of a neural network trained using labeled training data
CN113128686A (en) * 2020-01-16 2021-07-16 华为技术有限公司 Model training method and device
US20210334709A1 (en) * 2020-04-27 2021-10-28 International Business Machines Corporation Breadth-first, depth-next training of cognitive models based on decision trees
US12039418B2 (en) * 2020-07-27 2024-07-16 International Business Machines Corporation Incremental learning through reverse data distribution
US20220076142A1 (en) * 2020-09-08 2022-03-10 Darwinai Corporation System and method for selecting unlabled data for building learning machines
GB2600154A (en) * 2020-10-23 2022-04-27 Exscientia Ltd Drug optimisation by active learning
KR20230091156A (en) 2020-10-23 2023-06-22 엑스사이언티아 에이아이 리미티드 Drug Optimization by Active Learning
CN114724642B (en) * 2021-01-04 2025-08-19 腾讯科技（深圳）有限公司 Method, device, equipment and storage medium for determining compound attribute parameters
WO2023171774A1 (en) * 2022-03-10 2023-09-14 日本碍子株式会社 Material creation support system, method, and program
CN119137669A (en) * 2022-05-05 2024-12-13 巴斯夫欧洲公司 Methods for determining target synthesis specifications
CN116402480B (en) * 2023-06-07 2023-09-19 成都普朗克科技有限公司 Method and system for outputting inventory based on association rule self-built model
EP4564229A1 (en) * 2023-11-28 2025-06-04 Siemens Aktiengesellschaft Method and system for automatically generating labeled training data for supervised machine learning models for industrial equipment matching
US12368503B2 (en) 2023-12-27 2025-07-22 Quantum Generative Materials Llc Intent-based satellite transmit management based on preexisting historical location and machine learning
CN119066404B (en) * 2024-08-29 2025-05-02 西安西科测控设备有限责任公司 Method and system for automatically searching and checking coal and gas outburst early-warning criteria

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050278124A1 (en) 2004-06-14 2005-12-15 Duffy Nigel P Methods for molecular property modeling using virtual data
US8595154B2 (en) * 2011-01-26 2013-11-26 Google Inc. Dynamic predictive modeling platform
JP6133789B2 (en) 2011-02-14 2017-05-24 カーネギー メロン ユニバーシティ Machine learning based method, machine readable medium and electronic system for predicting the effect of a compound on an object

Also Published As

Publication number Publication date
US20210012862A1 (en) 2021-01-14
CN112136179A (en) 2020-12-25
CN112136179B (en) 2025-02-18
EP3776563A2 (en) 2021-02-17
WO2019186195A3 (en) 2019-12-12
US12094578B2 (en) 2024-09-17
WO2019186195A2 (en) 2019-10-03

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